A geologic model is a computer-based representation of a region of the earth subsurface, such as a petroleum reservoir. A geologic model commonly consists of a 3-D geocellular grid that is composed of contiguous 3-D cells, to which properties such as lithology, porosity, permeability or water saturation are assigned using various algorithms, e.g., geostatistical algorithms. The model can be used for many purposes, but is most commonly used as an input to computer programs that simulate the movement of fluids within the subsurface region. These programs are used to predict, for example, hydrocarbon production rates and volumes from a petroleum reservoir over time.
When sedimentary deposits are constructed by discrete elements/objects such as channels and lobes, they often form a characteristic depositional architecture that controls the three dimensional (3D) structure of the strata. This characteristic depositional architecture is called a stacking pattern. For example, a pro-gradational stacking pattern describes objects such as lobes stacking in a forward (paleo-flow) direction with little lateral movement. When flow barriers such as shale drape are deposited along the channel or lobe bounding surfaces, the proper representation of the spatial arrangement of these discrete objects or the stacking pattern is important because they control the flow barrier distribution and hence have significant effect on the movement of fluids in the reservoir. Current technology is not able to construct geologic models that precisely represent the interpreted or conceived depositional stacking pattern while at the same time being efficiently conditioned to the available data. This problem can be very costly if the model-based predictions are used as a basis for making high-expense business decisions, such as decisions related to drilling and completing wells, and to constructing surface facilities to handle produced hydrocarbons.
Pyrcz, et al. (2005) describes a surface-based method for constructing geologic models of reservoirs by reproducing the geometries and stacking patterns of flow-event deposits in turbidite lobes. The individual deposits are defined by stochastically modeling their bounding surfaces, and rules are established to constrain how the individual deposits stack within the model. The method of Pyrcz et al. (2005) is a stochastic method that has similarity to object-based algorithms, thus likely has similar limitations on data conditioning.
Michael et al. (2009) proposed a methodology that incorporates multiple simulation techniques to produce a model that mimics the architecture of the process-based model and is conditioned to well data only. First, the geologic features of grain size, or facies, and distributions simulated by a process-based model are analyzed, and statistics of feature geometry are extracted. The statistics are used to generate multiple realizations of reduced-dimensional features using an object-based technique. These realizations are used as multiple alternative training images in multiple-point geostatistical simulation (MPS), which is conditioned to well data. Successive realizations of individual strata/objects are generated in depositional order, each dependent on previously-simulated geometry, and stacked to produce a three-dimensional facies model that mimics the architecture of the process-based model. In the approach presented by Michael et al. (2009), MPS simulation is applied to sequentially simulate individual objects, not the whole stacked package. This pixel-based approach is flexible for conditioning individual well data, but is difficult in constraining a gross thickness map, such as seismic-derived gross thickness maps or zone thickness maps.